fix bfgs_doc (#41505)

* fix bfgs_doc; test=document_fix

* add parameter name; test=document_fix

* modify according to chenlong's comments;test=document_fix

python/paddle/incubate/optimizer/functional/bfgs.py

@@ -33,63 +33,43 @@ def minimize_bfgs(objective_func,
                   name=None):
     r"""
     Minimizes a differentiable function `func` using the BFGS method.
-    The BFGS is a quasi-Newton method for solving an unconstrained
-    optimization problem over a differentiable function.
-    Closely related is the Newton method for minimization. Consider the iterate 
-    update formula
+    The BFGS is a quasi-Newton method for solving an unconstrained optimization problem over a differentiable function.
+    Closely related is the Newton method for minimization. Consider the iterate update formula:
+
     .. math::
-        x_{k+1} = x_{k} + H \nabla{f},
-    If $H$ is the inverse Hessian of $f$ at $x_{k}$, then it's the Newton method.
-    If $H$ is symmetric and positive definite, used as an approximation of the inverse Hessian, then 
+        x_{k+1} = x_{k} + H_k \nabla{f_k}
+
+    If :math:`H_k` is the inverse Hessian of :math:`f` at :math:`x_k`, then it's the Newton method.
+    If :math:`H_k` is symmetric and positive definite, used as an approximation of the inverse Hessian, then 
     it's a quasi-Newton. In practice, the approximated Hessians are obtained
     by only using the gradients, over either whole or part of the search 
-    history, the former is BFGS.
+    history, the former is BFGS, the latter is L-BFGS.
 
     Reference: 
-        Jorge Nocedal, Stephen J. Wright, Numerical Optimization, Second Edition, 2006.
-        pp140: Algorithm 6.1 (BFGS Method).
-    
-    Following summarizes the the main logic of the program based on BFGS. Note: _k represents value of
-    k_th iteration, ^T represents the transposition of a vector or matrix.
-    repeat
-        p_k = H_k * g_k
-        alpha = strong_wolfe(f, x_k, p_k)
-        x_k+1 = x_k + alpha * p_k
-        s_k = x_k+1 - x_k
-        y_k = g_k+1 - g_k
-        rho_k = 1 / (s_k^T * y_k)
-        V_k^T = I - rho_k * s_k * y_k^T
-        V_k = I - rho_k * y_k * s_k^T
-        H_k+1 = V_k^T * H_k * V_k + rho_k * s_k * s_k^T
-        check_converge
-    end 
+        Jorge Nocedal, Stephen J. Wright, Numerical Optimization, Second Edition, 2006. pp140: Algorithm 6.1 (BFGS Method).
 
     Args:
-        objective_func: the objective function to minimize. ``func`` accepts
-            a multivariate input and returns a scalar.
-        initial_position (Tensor): the starting point of the iterates. For methods like Newton and quasi-Newton 
-        the initial trial step length should always be 1.0.
-        max_iters (int): the maximum number of minimization iterations.
-        tolerance_grad (float): terminates if the gradient norm is smaller than this. Currently gradient norm uses inf norm.
-        tolerance_change (float): terminates if the change of function value/position/parameter between 
-            two iterations is smaller than this value.
-        initial_inverse_hessian_estimate (Tensor): the initial inverse hessian approximation at initial_position.
-        It must be symmetric and positive definite.
-        line_search_fn (str): indicate which line search method to use, only support 'strong wolfe' right now. May support 
-            'Hager Zhang' in the futrue.
-        max_line_search_iters (int): the maximum number of line search iterations.
-        initial_step_length (float): step length used in first iteration of line search. different initial_step_length 
-        may cause different optimal result.
-        dtype ('float32' | 'float64'): In static graph, float64 will be convert to float32 due to paddle.assign limit.
+        objective_func: the objective function to minimize. ``objective_func`` accepts a multivariate input and returns a scalar.
+        initial_position (Tensor): the starting point of the iterates. 
+        max_iters (int, optional): the maximum number of minimization iterations. Default value: 50.
+        tolerance_grad (float, optional): terminates if the gradient norm is smaller than this. Currently gradient norm uses inf norm. Default value: 1e-7.
+        tolerance_change (float, optional): terminates if the change of function value/position/parameter between two iterations is smaller than this value. Default value: 1e-9.
+        initial_inverse_hessian_estimate (Tensor, optional): the initial inverse hessian approximation at initial_position. It must be symmetric and positive definite. Default value: None.
+        line_search_fn (str, optional): indicate which line search method to use, only support 'strong wolfe' right now. May support 'Hager Zhang' in the futrue. Default value: 'strong wolfe'.
+        max_line_search_iters (int, optional): the maximum number of line search iterations. Default value: 50.
+        initial_step_length (float, optional): step length used in first iteration of line search. different initial_step_length may cause different optimal result. For methods like Newton and quasi-Newton the initial trial step length should always be 1.0. Default value: 1.0.
+        dtype ('float32' | 'float64', optional): data type used in the algorithm. Default value: 'float32'.
+        name (str, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default value: None.
 
     Returns:
-        is_converge (bool): Indicates whether found the minimum within tolerance.
-        num_func_calls (int): number of objective function called.
-        position (Tensor): the position of the last iteration. If the search converged, this value is the argmin of 
-        the objective function regrading to the initial position.
-        objective_value (Tensor): objective function value at the `position`.
-        objective_gradient (Tensor): objective function gradient at the `position`.
-        inverse_hessian_estimate (Tensor): the estimate of inverse hessian at the `position`.
+        output(tuple):
+
+            - is_converge (bool): Indicates whether found the minimum within tolerance.
+            - num_func_calls (int): number of objective function called.
+            - position (Tensor): the position of the last iteration. If the search converged, this value is the argmin of the objective function regrading to the initial position.
+            - objective_value (Tensor): objective function value at the `position`.
+            - objective_gradient (Tensor): objective function gradient at the `position`.
+            - inverse_hessian_estimate (Tensor): the estimate of inverse hessian at the `position`.
 
     Examples:
         .. code-block:: python

python/paddle/incubate/optimizer/functional/lbfgs.py

@@ -32,53 +32,45 @@ def minimize_lbfgs(objective_func,
                    initial_step_length=1.0,
                    dtype='float32',
                    name=None):
-    r"""Minimizes a differentiable function `func` using the L-BFGS method.
-    The L-BFGS is simalar as BFGS, the only difference is that L-BFGS use historical
-    sk, yk, rhok rather than H_k-1 to compute Hk.
+    r"""
+    Minimizes a differentiable function `func` using the L-BFGS method.
+    The L-BFGS is a quasi-Newton method for solving an unconstrained optimization problem over a differentiable function.
+    Closely related is the Newton method for minimization. Consider the iterate update formula:
+
+    .. math::
+        x_{k+1} = x_{k} + H_k \nabla{f_k}
+
+    If :math:`H_k` is the inverse Hessian of :math:`f` at :math:`x_k`, then it's the Newton method.
+    If :math:`H_k` is symmetric and positive definite, used as an approximation of the inverse Hessian, then 
+    it's a quasi-Newton. In practice, the approximated Hessians are obtained
+    by only using the gradients, over either whole or part of the search 
+    history, the former is BFGS, the latter is L-BFGS.
+
     Reference:
-        Jorge Nocedal, Stephen J. Wright, Numerical Optimization, Second Edition, 2006.
-        pp179: Algorithm 7.5 (L-BFGS).
-
-    Following summarizes the the main logic of the program based on L-BFGS.Note: _k represents 
-    value of k_th iteration, ^T represents the transposition of a vector or matrix.
-    repeat
-        compute p_k by two-loop recursion
-        alpha = strong_wolfe(f, x_k, p_k)
-        x_k+1 = x_k + alpha * p_k
-        s_k = x_k+1 - x_k
-        y_k = g_k+1 - g_k
-        rho_k = 1 / (s_k^T * y_k)
-        update sk_vec, yk_vec, rhok_vec
-        check_converge
-    end 
+        Jorge Nocedal, Stephen J. Wright, Numerical Optimization, Second Edition, 2006. pp179: Algorithm 7.5 (L-BFGS).
 
     Args:
-        objective_func: the objective function to minimize. ``func`` accepts
-            a multivariate input and returns a scalar.
-        initial_position (Tensor): the starting point of the iterates. For methods like Newton and quasi-Newton 
-        the initial trial step length should always be 1.0 .
-        history_size (Scalar): the number of stored vector pairs {si,yi}.
-        max_iters (Scalar): the maximum number of minimization iterations.
-        tolerance_grad (Scalar): terminates if the gradient norm is smaller than
-            this. Currently gradient norm uses inf norm.
-        tolerance_change (Scalar): terminates if the change of function value/position/parameter between 
-            two iterations is smaller than this value.
-        initial_inverse_hessian_estimate (Tensor): the initial inverse hessian approximation.
-        line_search_fn (str): indicate which line search method to use, only support 'strong wolfe' right now. May support 
-            'Hager Zhang' in the futrue.
-        max_line_search_iters (Scalar): the maximum number of line search iterations.
-        initial_step_length: step length used in first iteration of line search. different initial_step_length 
-        may cause different optimal result.
-        dtype ('float' | 'float32' | 'float64' | 'double'): the data
-            type to be used.
+        objective_func: the objective function to minimize. ``objective_func`` accepts a multivariate input and returns a scalar.
+        initial_position (Tensor): the starting point of the iterates. 
+        history_size (Scalar): the number of stored vector pairs {si,yi}. Default value: 100.
+        max_iters (int, optional): the maximum number of minimization iterations. Default value: 50.
+        tolerance_grad (float, optional): terminates if the gradient norm is smaller than this. Currently gradient norm uses inf norm. Default value: 1e-7.
+        tolerance_change (float, optional): terminates if the change of function value/position/parameter between two iterations is smaller than this value. Default value: 1e-9.
+        initial_inverse_hessian_estimate (Tensor, optional): the initial inverse hessian approximation at initial_position. It must be symmetric and positive definite. Default value: None.
+        line_search_fn (str, optional): indicate which line search method to use, only support 'strong wolfe' right now. May support 'Hager Zhang' in the futrue. Default value: 'strong wolfe'.
+        max_line_search_iters (int, optional): the maximum number of line search iterations. Default value: 50.
+        initial_step_length (float, optional): step length used in first iteration of line search. different initial_step_length may cause different optimal result. For methods like Newton and quasi-Newton the initial trial step length should always be 1.0. Default value: 1.0.
+        dtype ('float32' | 'float64', optional): data type used in the algorithm. Default value: 'float32'.
+        name (str, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default value: None.
 
     Returns:
-        is_converge (bool): Indicates whether found the minimum within tolerance.
-        num_func_calls (int): number of objective function called.
-        position (Tensor): the position of the last iteration. If the search converged, this value is the argmin of 
-        the objective function regrading to the initial position.
-        objective_value (Tensor): objective function value at the `position`.
-        objective_gradient (Tensor): objective function gradient at the `position`.
+        output(tuple):
+
+            - is_converge (bool): Indicates whether found the minimum within tolerance.
+            - num_func_calls (int): number of objective function called.
+            - position (Tensor): the position of the last iteration. If the search converged, this value is the argmin of the objective function regrading to the initial position.
+            - objective_value (Tensor): objective function value at the `position`.
+            - objective_gradient (Tensor): objective function gradient at the `position`.
             
     Examples:
         .. code-block:: python